Differentially expressed genes in CLL

Aim: Find gene signatures for mutational status of CLL patients

load packages

library(DESeq2)
library(dplyr)
library(magrittr)
library(tidyverse)
library(gridExtra)
library(ComplexHeatmap)
library(matrixStats)
library(here)
library(reshape2)
library(DT)
library(xlsx)

load data

data_dir <- here("data")
output_dir <- here("output")
figure_dir <- here("output/figures")

#dds data set. gene expression data + patmetadata
load(paste0(data_dir, "/ddsrnaCLL_150218.RData"))

#load meta data including genotyping info
load(paste0(data_dir, "/patmeta_170324.RData"))
excluded_columns <- c("HIPO.ID","PID", "gender","project", "diagnosis", "date.of.diagnosis", "date.of.first.treatment", "IGHV.status","Methylation_Cluster")

#get pretreatment status
pretreat = read.csv(paste0(data_dir, "/diagnosis_information.csv"), sep = ";")
rownames(pretreat) <- pretreat$Patient.ID

patMeta <- as.tibble(patMeta) %>% filter(Patient.ID %in% ddsCLL$PatID) %>% dplyr::select(-one_of(excluded_columns))

Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
This warning is displayed once per session.

variants <- patMeta %>% dplyr::select(-Patient.ID) %>%  dplyr::select(-treatment) %>%
  mutate_if(is.factor, as.character) %>% 
  mutate_if(is.character, as.numeric) %>% 
  dplyr::select(colnames(.)[colSums(.,na.rm = TRUE) > 4]) %>% 
  colnames()

var_add <- variants[!variants %in% names(colData(ddsCLL))] 
rownames(patMeta) <-  patMeta$Patient.ID

Warning: Setting row names on a tibble is deprecated.

patMeta <- patMeta[colData(ddsCLL)$PatID,]
pretreat <- pretreat[colData(ddsCLL)$PatID,]
cd <- cbind(colData(ddsCLL), patMeta[,var_add])
colData(ddsCLL) <- cd
colData(ddsCLL)$pretreat <- as.factor(pretreat$pretreat)

###Deseq
ddsCLL <- estimateSizeFactors(ddsCLL)

#write a function to perform deseq for different genetic conditions 
diff <- function(cond){
  gc()
  ddsCLL_new <- ddsCLL[,!is.na(colData(ddsCLL)[,cond])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"IGHV"])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"pretreat"])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"trisomy12"])]
  colData(ddsCLL_new)[,"IGHV"] <-droplevels(colData(ddsCLL_new)[,"IGHV"])
  design(ddsCLL_new) <- as.formula(paste("~ IGHV + trisomy12 + pretreat +", paste(cond)))
  rnaRaw <- DESeq(ddsCLL_new, betaPrior = FALSE)
  res <- results(rnaRaw)
  resOrdered <- res[order(res$pvalue),]
}

#gene_conditions <- c("del13q14", "del8p12", "gain8q24", "del11q22.3", "del17p13", "BRAF", "NOTCH1", "SF3B1", "TP53", "ATM", "MED12", "trisomy12")
variants_not12 <- variants[!variants %in% "trisomy12"]
res_list <- lapply(variants_not12, diff)

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 880 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 870 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1028 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1178 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 853 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 846 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 921 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 894 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 978 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1106 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 978 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1062 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1005 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 935 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 914 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 935 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 954 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 856 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 992 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 934 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 982 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 963 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 966 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

names(res_list) <- variants_not12

diff_notri12 <- function(cond){
  ddsCLL_new <- ddsCLL[,!is.na(colData(ddsCLL)[,cond])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"trisomy12"])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"pretreat"])]
  design(ddsCLL_new) <- as.formula(paste("~ trisomy12 + pretreat + ", paste(cond)))
  rnaRaw <- DESeq(ddsCLL_new, betaPrior = FALSE)
  res <- results(rnaRaw)
  resOrdered <- res[order(res$pvalue),]
}

res_list[["IGHV"]] <- diff_notri12("IGHV")

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1183 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

diff_noighv <- function(cond){
  ddsCLL_new <- ddsCLL[,!is.na(colData(ddsCLL)[,cond])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"IGHV"])]
  ddsCLL_new <- ddsCLL_new[,!is.na(colData(ddsCLL_new)[,"pretreat"])]
  design(ddsCLL_new) <- as.formula(paste("~ IGHV + pretreat + ", paste(cond)))
  rnaRaw <- DESeq(ddsCLL_new, betaPrior = FALSE)
  res <- results(rnaRaw)
  resOrdered <- res[order(res$pvalue),]
}

res_list[["trisomy12"]] <- diff_noighv("trisomy12")

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 1183 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

save(res_list, file=paste0(output_dir,"/desRes_020422.RData"))

load(paste0(output_dir,"/desRes_020422.RData"))

Diff genes

pCut <- 0.05

difftab <- function(condition){
  dataTab <- data.frame(res_list[[condition]])
  dataTab$ID <- rownames(dataTab)
  #filter using pvalues
  dataTab <- filter(dataTab, padj <= pCut) %>%
    arrange(padj) %>%
    mutate(Symbol = rowData(ddsCLL[ID,])$symbol) %>%
    filter(abs(log2FoldChange) > 1)
  dataTab <- dataTab[!duplicated(dataTab$Symbol),]
  dataTab <- dataTab[!is.na(dataTab$Symbol),]
  rownames(dataTab) <- dataTab$ID
  write.csv(dataTab, file=paste0(output_dir,"/diff_genes/pretreatment/", condition, "_diffGenes.csv"))
  dataTab
}

cond <- c(variants, "IGHV")

#Only run when you want to write result tables! Change path according to test! 
sigRes <- lapply(cond, difftab)
names(sigRes) <- cond

for (condition in cond) {
  cat("### ", condition, " {-}\n")
  
  DESeq2::plotMA(res_list[[condition]], ylim=c(-5,5), main= paste(condition))
  cat("\n\n")
}

# myMaPlot <-function(condition){
#   DESeq2::plotMA(res_list[[condition]], ylim=c(-5,5), main= paste(condition))
# }
# 
# lapply(cond, myMaPlot)

for (condition in cond) {
  cat("### ", condition, " {-}\n")
  
  res <- res_list[[condition]]
  hist(res$pvalue, breaks=100, col="skyblue", border="slateblue",
                     main=paste0("Histogramm of pvalues:", condition),plot = TRUE)
  cat("\n\n")
}

# myHist <- function(condition){
#   res <- res_list[[condition]]
#   hist(res$pvalue, breaks=100, col="skyblue", border="slateblue",
#                     main=paste0("Histogramm of pvalues:", condition),plot = TRUE)
# }
# 
# hist_cond <- lapply(cond, myHist)

dataTab <- data.frame(res_list[["del8p12"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["gain8q24"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["del11q22.3"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["trisomy12"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["del13q14"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["del17p13"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["BRAF"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["NOTCH1"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["SF3B1"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["TP53"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["ATM"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["MED12"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

dataTab <- data.frame(res_list[["IGHV"]])
dataTab$Symbol <- rowData(ddsCLL[rownames(dataTab),])$symbol
dataTab <- dataTab[!dataTab$Symbol %in% c("",NA),]
datatable(dataTab, filter = 'top', options = list(
  pageLength = 5, autoWidth = TRUE
)) %>% formatRound(1:(ncol(dataTab)-1), 2)

wb <- createWorkbook()
sheets <- lapply(cond, createSheet, wb = wb)
void <- Map(addDataFrame, sigRes, sheets)
saveWorkbook(wb, file = paste0(output_dir,"/de_genes_all_pretreatment.xlsx"))

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.7 LTS

Matrix products: default
BLAS:   /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=de_DE.UTF-8          LC_NUMERIC=C                 
 [3] LC_TIME=de_DE.UTF-8           LC_COLLATE=de_DE.UTF-8       
 [5] LC_MONETARY=de_DE.UTF-8       LC_MESSAGES=de_DE.UTF-8      
 [7] LC_PAPER=de_DE.UTF-8          LC_NAME=de_DE.UTF-8          
 [9] LC_ADDRESS=de_DE.UTF-8        LC_TELEPHONE=de_DE.UTF-8     
[11] LC_MEASUREMENT=de_DE.UTF-8    LC_IDENTIFICATION=de_DE.UTF-8

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] xlsx_0.6.5                  DT_0.17                    
 [3] reshape2_1.4.3              here_0.1                   
 [5] ComplexHeatmap_2.0.0        gridExtra_2.3              
 [7] forcats_0.4.0               stringr_1.4.0              
 [9] purrr_0.3.2                 readr_1.3.1                
[11] tidyr_0.8.3                 tibble_2.1.3               
[13] ggplot2_3.1.1               tidyverse_1.2.1            
[15] magrittr_1.5                dplyr_0.8.1                
[17] DESeq2_1.24.0               SummarizedExperiment_1.14.0
[19] DelayedArray_0.10.0         BiocParallel_1.18.0        
[21] matrixStats_0.54.0          Biobase_2.44.0             
[23] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
[25] IRanges_2.18.1              S4Vectors_0.22.0           
[27] BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
 [1] colorspace_1.4-1       rjson_0.2.20           rprojroot_1.3-2       
 [4] circlize_0.4.6         htmlTable_1.13.1       XVector_0.24.0        
 [7] GlobalOptions_0.1.0    base64enc_0.1-3        fs_1.3.1              
[10] clue_0.3-57            rstudioapi_0.10        bit64_0.9-7           
[13] AnnotationDbi_1.46.0   lubridate_1.7.4        xml2_1.2.0            
[16] splines_3.6.3          geneplotter_1.62.0     knitr_1.23            
[19] Formula_1.2-3          jsonlite_1.6           workflowr_1.4.0       
[22] rJava_0.9-13           broom_0.5.2            annotate_1.62.0       
[25] cluster_2.1.2          png_0.1-7              shiny_1.3.2           
[28] compiler_3.6.3         httr_1.4.0             backports_1.1.4       
[31] assertthat_0.2.1       Matrix_1.3-4           lazyeval_0.2.2        
[34] cli_1.1.0              later_0.8.0            acepack_1.4.1         
[37] htmltools_0.3.6        tools_3.6.3            gtable_0.3.0          
[40] glue_1.3.1             GenomeInfoDbData_1.2.1 Rcpp_1.0.1            
[43] cellranger_1.1.0       nlme_3.1-152           crosstalk_1.0.0       
[46] xfun_0.7               xlsxjars_0.6.1         rvest_0.3.4           
[49] mime_0.7               XML_3.98-1.20          zlibbioc_1.30.0       
[52] scales_1.0.0           promises_1.0.1         hms_0.4.2             
[55] RColorBrewer_1.1-2     yaml_2.2.0             memoise_1.1.0         
[58] rpart_4.1-15           latticeExtra_0.6-28    stringi_1.4.3         
[61] RSQLite_2.1.1          genefilter_1.66.0      checkmate_1.9.3       
[64] shape_1.4.4            rlang_0.3.4            pkgconfig_2.0.2       
[67] bitops_1.0-6           evaluate_0.14          lattice_0.20-38       
[70] htmlwidgets_1.3        bit_1.1-14             tidyselect_0.2.5      
[73] plyr_1.8.4             R6_2.4.0               generics_0.0.2        
[76] Hmisc_4.2-0            DBI_1.0.0              pillar_1.4.1          
[79] haven_2.1.0            foreign_0.8-76         withr_2.1.2           
[82] survival_2.44-1.1      RCurl_1.95-4.12        nnet_7.3-16           
[85] modelr_0.1.4           crayon_1.3.4           rmarkdown_1.13        
[88] GetoptLong_0.1.7       locfit_1.5-9.1         readxl_1.3.1          
[91] data.table_1.12.2      blob_1.1.1             git2r_0.25.2          
[94] digest_0.6.19          xtable_1.8-4           httpuv_1.5.1          
[97] munsell_0.5.0